The utilization of digital tools in agricultural extension has facilitated information delivery through non-face-to-face interactions. Therefore, this study aimed to map the variation in digital tools used by agricultural extension workers to access and deliver information and analyse the outcomes of farmers’ adoption. Data were collected through in-depth interviews with agricultural extension workers at 11 Agricultural Extension Centers. The data were processed using the N-Vivo qualitative data analysis software. The results showed that extension workers combined various digital tools as sources of extension materials and channels for delivering information to farmers. Although social interaction between agricultural extension workers and farmers occurred non-face-to-face, messages could be adopted by farmers and yield tangible outcomes. This was reflected in the asynchronous communication, allowing extension workers sufficient time to improve the quality of the delivered messages. Farmers also had sufficient time to review the received information content in this context repeatedly. These results implied that although extension content is delivered through non-face-to-face interaction, it can still drive adoption with significant outcomes.

The ability to take advantage of new digital solutions and technology will give companies a competitive edge, and operational optimization remains a major concern. A significant area of risk is cyber security because software-based technologies are integral to ship operations. Particular emphasis has been placed on the vulnerabilities of the Global Navigation Satellite System (GNSS), since it is an essential part of many maritime facilities and hence a target for hackers. Presently, research has shown that increased integration of new enabling technologies, like the Internet of Things (IoT) and big data, is driving the dramatic proliferation of cybercrimes. However, most of the attacks are related to ransomware attacks and/or with direct attack to the information technology (IT) and infrastructure. Nevertheless, there is a strong trend toward increased systems integration, which will produce substantial business value by making it easier to operate autonomous vessels, utilizing smart ports more, reducing the need for labour, and improving economic stability and service efficiency. Cybersecurity is becoming more and more important as a result of the quick digital transformation of the offshore and maritime sectors, which has also brought new dangers and laws. The marine sector has started to take cybersecurity seriously in light of the multiple documented instances of cyberattacks that have exposed business or personal data, caused large financial losses, and caused other problems. However, the body of existing research on emerging threats in maritime cyberspace is either inadequate or ignores important variables. Based on the most recent developments in the maritime sector, the article presents a classification of the most serious cyberthreats as well as the risks to cybersecurity in maritime operations and possible mitigation strategies from an educational research perspective.

Within the last four years, Lithuania has faced different foreign policy challenges due to geopolitical situations such as the Ukraine-Russia war, the migration crisis on the border with Belarus, and the conflict with China. After opening a Taiwanese representative office in Vilnius, China downgraded diplomatic relations with Lithuania. The purpose of the article is to assess the impact of the changes on international economic relations between Lithuania and China. The paper employs descriptive statistics, correlation-regression, sensitivity analysis, and agglomerative hierarchical cluster analysis. The research is based on the impact of international economic relations on international trade by analyzing separately imports and exports. Our research fills a gap in international relations and globalization theory by focusing on international collaboration between small and large countries, while the large country implements economic sanctions. In the context of Lithuania, exports to China and imports from China comprise a small percentage in the structure of international trade. Lithuania’s GDP level reacts sensitively to changes in export and import data only if they change drastically (over 50%).

Poverty, as a phenomenon, remains an obstacle to global sustainable development. Although a universal malaise, it is more prevalent in underdeveloped countries, including Nigeria. However, because of its devastating impacts on the Nigerian economy, such as increasing death rates, high crime rates, insecurity difficulties, threats to national cohesion, and so on, successive administrations have implemented poverty alleviation programs to mitigate the consequences of this disease. Worryingly, despite a multiplicity of projects and massive human and natural resources invested to match global standards, Nigeria remains impoverished. The curiosity at how these programs fail, either because of implementation hiccups or because elites’ wealth and power influence these programs spurred the paper to assess poverty alleviation policies and elitist approaches in Nigeria. The study employed the desk study approach, as it examined secondary sources such as books, journals, articles, and magazines. Its theoretical underpinning was the elite theory. The paper discovered that several factors such as corruption, the elitist nature of the policies which in disguise reflect public interests, lack of continuity, lack of coordination and monitoring system, misappropriation of public resources, and others, led to the poor performances of government in alleviating poverty in Nigeria. The paper concludes that, while the rate of poverty index in Nigeria rises year after year, poverty alleviation efforts in Nigeria have had little or no influence on the Nigerian economy, since most of these projects are purely reflective of the elites’ interests rather than the masses. Therefore, the paper recommends that for there to be a reduction in poverty incidence in Nigeria, a holistic developmental approach should be adopted, the policies formulated and implemented should sync with the needs of the citizens, and quality and viable programs should be sustained and financed irrespective of change in government; public accountability should be instilled; proper coordination and monitoring system should be domesticated, etc.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.